Generating physical product customization parameters based on analysis of input media

ABSTRACT

An enhanced product recommendation service observes a user engaging in an activity to automatically recommend products that facilitate performance of the activity. Photographs and/or video of the user performing the activity may be analyzed to identify an output that results from the activity and/or an activity task sequence that includes multiple tasks associated with completing the activity. Then, the enhanced product recommendation service may identify a product that is usable to generate the output(s) of the activity and/or complete the activity without performing one or more individual tasks of the activity task sequence. The product may be an existing product. Alternatively, the product may be a customized product that is designed based on observing the user engage in the activity. Physical measurements of the customized product may be determined based on various measurements determined by analyzing the photographs and/or video of the user performing the activity.

PRIORITY APPLICATION

This application claims priority to U.S. patent application Ser. No.16/040,351, filed Jul. 19, 2018, and U.S. Provisional Application No.62/630,192, filed Feb. 13, 2018, the contents of which are incorporatedherein by reference in their entireties.

BACKGROUND

Some services are designed for seamless integration into an environmentfor streamlining tasks that a user sets out to perform. For example, asmart speaker may be physically introduced into an environment toreceive questions from the user and, ultimately, to provide appropriateresponses back to the user in real time. Some smart speakers includecameras for facilitating video calls or even observing the user'sclothing to provide fashion advice using various fashion-based machinelearning algorithms. Generally, these services provide the user withinformation only in response to specific questions that are asked by theuser. For example, the user may ask a question about a cookingmeasurement to prompt the smart speaker to provide the user with someknowingly desired piece of information. Thus, by enabling the user toverbally ask the question, the smart speaker streamlines the task oflearning the cooking measurement (e.g., verbally asking the question isstreamlined as compared to manually looking the information up on theinternet).

Unfortunately, under a variety of circumstances the user may perform atask without knowledge of a product that is designed to make theperformance of the task easier. For example, the user may manuallyconvert a raw material into a finished product without knowledge of aproduct that is specifically designed to reduce the time and/or effortrequired to create the finished product. Under other circumstances, theuser may perform a task that could potentially be performed more easilyif a product, which does not yet exist, were customized with respect tothe task.

All too often, subsequent to and/or during performance of a task in aninefficient manner, users are lured into accessing an internet browserservice and generating exorbitant numbers of search queries in a questto identify product(s) for improving the ease of performing the task.Under circumstances where such a product does not yet exist, the networktraffic generated by these browsing activities is without values.Therefore, existing product recommendation services fail to mitigate theissue of wasteful usage of computing resources that frequently resultsfrom aimless and fruitless quests for non-existent products.

It is with respect to these and other considerations that the disclosuremade herein is presented.

SUMMARY

Technologies described herein enable an enhanced product recommendationservice to automatically recommend products to a user based onobservations taken of the user engaging in an activity. In someembodiments, user activity is monitored such as, for example, by takingphotos or video of a user engaging in an activity. For instance, photosor video might be captured of a user cooking a meal or interacting withan electronic device like a phone.

The observed activity can be analyzed using artificial intelligence(“AI”) techniques to identify one or more tasks that the user isperforming (e.g. taking a “selfie” with their phone or cooking a meal).An amount of time for completion of the tasks can also be determined. AItechniques can then also be utilized to generate parameters for a newproduct that can be utilized to reduce the number of tasks required tocomplete the activity or to reduce the amount of effort required tocomplete the tasks. For example, parameters for a selfie stick of acertain length might be generated based upon the observed manner inwhich a user takes a selfie. As another example, parameters for a phonecase with a shutter release button in a specific location might begenerated based upon the manner in which the user takes photos withtheir phone. A product can then be manufactured according to thegenerated parameters. A recommendation can also be provided to a userfor an existing product that can reduce the number of tasks required tocomplete the activity or reduce the amount of effort required tocomplete the tasks. The manufactured or recommended product can also beprovided to the user preemptively (i.e. without the user ordering orotherwise requesting the product).

By preemptively informing a user of products that can improve theirability to perform certain tasks, the techniques described hereindiminish the lure that users often succumb to aimlessly entering searchqueries to wander from web-page to web-page in search of obscure or,worse yet, non-existent products. Therefore, implementations of thetechniques described herein effectively reduce network bandwidth andconsumption of other computer resources such as, for example, processorcycles and memory that inherently results from submission of searchqueries to a web-based search engine.

Some embodiments analyze input media received from various sources toidentify an output that results from the activity and/or an activitytask sequence that includes multiple tasks associated with completingthe activity. As a specific but nonlimiting example, the input media mayinclude photographs and/or video taken of the user operating a kitchenknife to manually cut raw potatoes into French fries (e.g., elongatedrectangular sticks). In this example, the enhanced productrecommendation service may analyze the input media to identify that theuser is producing French fries as an output of an activity.Additionally, or alternatively, in this example, the enhanced productrecommendation service may identify that the user is performing anactivity task sequence including certain tasks such as obtaining a rawmaterial (e.g., whole potatoes), cutting the raw material into anintermediary output (e.g., potato slices of uniform thickness), and thencross cutting the intermediary output into a final output (e.g., Frenchfries).

Then, based on having identified the activity task sequence beingperformed by the user and/or the output(s) of the activity (e.g., theintermediary output and/or the final output), the enhanced productrecommendation service may identify a product (e.g., an existing productand/or a custom product that is not yet made) that is usable to generatethe output(s) of the activity and/or complete the activity withoutperforming one or more individual tasks of the activity task sequence.In various implementations, a product recommendation may then begenerated to inform the user of aspects (e.g., price, features, usertestimonials, etc.) of the identified product. Additionally, oralternatively, the identified product may be preemptively delivered tothe user (e.g., delivered to the user without being expressly ordered bythe user).

In an exemplary implementation, the enhanced product recommendationservice may identify an existing product that is usable in associationwith an activity that the user is observed as performing. For example,continuing with the scenario in which the input media is of the usermanually cutting French fries, the enhanced product recommendationservice may identify an existing product (e.g., a mandolin slicer thatis available from an online retailer) that is specifically designed forconverting the raw material (e.g., whole potatoes) into the output(e.g., French fries).

In order to observe the user performing an activity, the enhancedproduct recommendation service may receive input media that defines agraphical representation of a user engaging in the activity. Forexample, the input media may include photographs and/or video that arecaptured by a virtual assistant device (e.g., an AMAZON ECHO LOOK smartspeaker connected to AMAZON ALEXA, an APPLE IPHONE smart phone connectedto APPLE SIRI, etc.) that is set up in an environment where the userperforms the activity and is configured to communicate with anartificial intelligence based virtual assistant service (e.g., AMAZONALEXA, APPLE SIRI, etc.). The enhanced product recommendation servicemay then analyze the input media to identify a wide variety ofcharacteristics associated with the activity being performed by theuser. For example, the enhanced product recommendation service maydeploy machine learning techniques to implement an image recognitionmodel for determining one or more characteristics of the activity thatcan be identified from analyzing the photographs and/or video capturedby the virtual assistant device.

In some implementations, the enhanced product recommendation service mayidentify raw material characteristics associated with one or more rawmaterials that the user is using to perform the activity. For example,the enhanced product recommendation service may analyze the input mediato identify one or more materials that are graphically representedwithin the input media. Then, the enhanced product recommendationservice may determine whether the one or more “identified” materials arebeing used as an input for the activity. In some implementations, theenhanced product recommendation service may deploy machine learning toimplement the image recognition model for identifying differentmaterials that are included within the photographs and/or video capturedby the virtual assistant device. For example, continuing with thescenario in which the input media is of the user manually cutting Frenchfries, the enhanced product recommendation service may use the imagerecognition model to identify that whole potatoes are shown within theinput media. Based on the user interacting with an identified material,the enhanced product recommendation engine may determine whether theidentified material is a raw material that is being used as an input forthe activity.

In some implementations, the enhanced product recommendation service mayidentify output characteristics associated with one or more outputs thatare produced during performance of the activity by the user. Forexample, the enhanced product recommendation service may analyze theinput media to identify one or more products that are graphicallyrepresented within the input media. Then, the enhanced productrecommendation service may determine whether the one or more productsare produced as a result of the activity. For example, if a product doesnot exist when the user begins to perform the activity but rather isrecognized by the enhanced recommendation service only after the userhas performed one or more individual tasks of the activity tasksequence, the enhanced product recommendation service may determine(e.g., infer and/or conclude) that the product is produced as a resultof the activity. Stated differently, the enhanced product recommendationservice may determine that the product is an output of the userperforming the activity. In some implementations, the enhanced productrecommendation service may implement the image recognition model toidentify different outputs that are being produced within thephotographs and/or video captured by the virtual assistant device. Forexample, continuing with the scenario in which the input media is of theuser manually cutting French fries, the enhanced product recommendationservice may use the image recognition model to identify that Frenchfries are being produced within the input media.

In some implementations, the enhanced product recommendation service mayidentify task sequence characteristics associated with one or more tasksperformed by the user during performance of the activity. For example,the enhanced product recommendation service may analyze the input mediato identify one or more tasks that are being performed by the user inthe input media (e.g., within a photograph and/or video of the user). Insome implementations, tasks that are being performed by the user may beidentified based on one or more items of equipment that are graphicallyrepresented within the input media. For example, the enhanced productrecommendation service may determine that the user is performing aseries of cutting tasks based on identifying that the user ismanipulating a kitchen knife. In some implementations, tasks that arebeing performed by the user may be identified based on movementsperformed by the user with respect to an identified item of equipment,an identified raw material, and/or an identified output. For example,continuing with the scenario in which the input media is of the usermanually cutting French fries, the enhanced product recommendationservice may determine that the user is performing the series of cuttingtasks based on identifying that the user grips a whole potato (e.g., rawmaterial) with a left hand while manipulating a kitchen knife (e.g.,equipment) with a right hand to generate French fries (e.g., output).

Based on the characteristics that are associated with the activity andidentified by analyzing the input media of the user engaging in theactivity, the enhanced product recommendation service may determine anexisting product that is usable in association with the activity. Insome implementations, the enhanced product recommendation service mayanalyze product data that is associated with one or more retail servicesto identify an existing product that is usable for generating an outputthat results from the activity. For example, based on identifying thatthe user is slicing whole potatoes into conventionally shaped Frenchfries, the enhanced product recommendation service may identify anexisting mandolin slicer that is specifically designed for convertingwhole potatoes into conventionally shaped French fries. In someimplementations, the enhanced product recommendation service mayidentify multiple different existing products that are usable inassociation with the activity. For example, based on identifying thatthe user is slicing whole potatoes into conventionally shaped Frenchfries, the enhanced product recommendation service may identify both ofthe mandolin slicer and a lever-operated restaurant quality French frycutter.

In various implementations, a product recommendation may then begenerated to recommend the product to the user and/or to inform the userof parameters (e.g., price, features, user testimonials, etc.) of theidentified product. Generally, the product identified is designed toimprove the user's ability to easily perform the activity by, forexample, reducing the amount of time the activity takes, improvingergonomics of performing the activity, increasing an output rate of theactivity, eliminating a need to perform one or more tasks of adetermined activity task sequence, etc. For example, in response toanalyzing the input media and determining that the user is manuallycutting French fries, the enhanced product recommendation service maytransmit a product recommendation to a virtual assistant device and/oruser device associated with the user. An exemplary productrecommendation may inform the user of the product and/or benefits ofusing the product as opposed to performing the activity in the mannerobserved via the input media. Additionally, or alternatively, one ormore identified products may be delivered to the user preemptively(e.g., delivered to the user without being expressly ordered by theuser).

In another exemplary implementation, the enhanced product recommendationservice may determine product customization parameters for generating acustom product that is specifically designed based on analyzing theinput media. For example, a customized product can be designed based onvarious characteristics of the activity that are identified by analyzingthe input media. The product customization parameters may be determinedbased on output characteristics that indicate a specific shape and/orspecific dimensions of an output that results from the activity. As aspecific but non-limiting example, consider a scenario in which theoutput of the activity is substantially uniformly dimensionedtriangularly-shaped potato slices. In this example, the enhanced productrecommendation service may identify the shape and dimensions of theoutput potato slices and may generate product customization parametersfor generating a custom product that is specifically designed togenerate substantially similar potato slices to those manually createdby the user.

In some implementations, the enhanced product recommendation service mayschedule a preemptive delivery of the custom product to the user (e.g.,at a physical address of the user such as the home and/or office). Inthis way, the enhanced product recommendation service enables the userto receive products that are specifically customized to assist with anactivity that the user has been performing in a preemptive manner suchthat the user has neither ordered nor even expressed and/or contemplateda desire for the custom product. In various implementations, anotification may be generated to inform the user that a preemptivedelivery has been scheduled and to provide the user with the ability tocancel the preemptive delivery (e.g., to avoid being provided withand/or charged for the custom product).

AI may alternatively be utilized to generate one or more behavioralrecommendations to provide to the user to optimize performance of thetask by simply modifying the user's behavior in performing the task.This modified behavior is typically more efficient and/or less dangerousthan the user's current behavior, and may be used instead of, or in theinterim of receiving the ordered/customized product.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intendedthat this Summary be used to limit the scope of the claimed subjectmatter. Furthermore, the claimed subject matter is not limited toimplementations that solve any or all disadvantages noted in any part ofthis disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The Detailed Description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Thesame reference numbers in different figures indicate similar oridentical items.

FIG. 1 illustrates an exemplary system for analyzing input media of auser engaging in an activity to determine the activity being performedand to then identify a product that is usable in association with theactivity.

FIG. 2 illustrates exemplary input media that is suitable for analysisby an activity identification engine to determine exemplary activitycharacteristics data.

FIG. 3 illustrates an exemplary system for analyzing input media of auser engaging in an activity and determining physical measurementsassociated with various aspects of the activity. Based on the identifiedphysical measurements, the system may determine product customizationparameters for generating a customized product that is designed toassist the user in performing the activity.

FIG. 4A illustrates exemplary input media that is suitable for analysisby an enhanced product recommendation service to determine exemplaryproduct customization parameters for generating a customized productthat is designed to assist the user in performing an activity beingperformed in the exemplary input media.

FIG. 4B illustrates an exemplary customized product that is specificallydesigned by the enhanced product recommendation service based on ananalysis of the exemplary input media discussed in relation to FIG. 4A.

FIG. 5 illustrates an example process of analyzing input media toidentify an activity being performed by a user and, based thereon,identifying one or more existing products that facilitate performance ofthe identified activity.

FIG. 6 illustrates an example process of analyzing input media togenerate activity characteristics data associated with an activity andphysical measurements data associated with the activity and, basedthereon, to generate product customization parameters for generating acustomized product that facilitates performance of the activity.

FIG. 7 shows additional details of an example computer architecture fora computer capable of executing the functionalities described hereinsuch as, for example, those described with reference to the enhancedproduct recommendation service, or any program components thereof asdescribed herein.

DETAILED DESCRIPTION

The following Detailed Description describes technologies that enable anenhanced product recommendation service to automatically recommendproducts to a user based on observations taken of the user engaging inan activity. Generally described, some embodiments analyze input mediaof a user engaging in an activity to identify an output that resultsfrom the activity and/or an activity task sequence that includesmultiple tasks associated with completing the activity. Then, based onhaving identified the activity task sequence being performed by the userand/or the output(s) of the activity, various embodiments identify aproduct (e.g., an existing product and/or a custom product that is notyet made) that is usable to generate the output(s) of the activityand/or complete the activity without performing one or more individualtasks of the activity task sequence. In various implementations, aproduct recommendation may then be generated to inform the user ofaspects (e.g., price, features, user testimonials, etc.) of theidentified product. Additionally, or alternatively, the identifiedproduct may be preemptively delivered to the user (e.g., delivered tothe user without being expressly ordered by the user).

The disclosed techniques are believed to be applicable to a variety ofscenarios in which a computing device such as a virtual assistant and/orsmart phone can generate media of a user engaging in an activity andthen provide that media to the enhanced product recommendation service(e.g., via a network connection). Aspects of the techniques disclosedbelow are predominantly described in the context of several specificexamples which are provided for illustrative purposes only. For example,aspects are described in the context of a user converting a raw materialinto a finished product (e.g., cutting French fries) and/or a userperforming an activity which does not result in any tangible materialconversion (e.g., juggling). Various aspects of the disclosed techniquesare, however, widely applicable to other scenarios. Thus, it can beappreciated that any other scenario that is suitable for observing auser via an electronic device (e.g., a camera on a virtual assistant)and then identifying a product that can assist with the activity iswithin the scope of the present disclosure.

Turning now to FIG. 1, illustrated is an exemplary system 100 foranalyzing input media 110 of a user 124 engaging in an activity todetermine the type of activity being performed and to then identify aproduct 134 that is usable in association with the activity. Asillustrated, the system 100 may include an electronic device (e.g. asmartphone and/or a virtual assistant device) that is set up within aphysical environment to observe the user 124 engaging in an activity.For purposes of the discussion of FIG. 1, the user 124 is shown to beworking on (e.g., slicing, etc.) a raw material to convert the rawmaterial from a raw form to a finished form. More specifically, the user124 is shown to be using a kitchen knife to slice whole potatoes intoFrench fries. In the illustrated example, the input media 110 is beinggenerated by a virtual assistant device 126 that is configured with acamera for generating photographs 112 and/or video 114 of a field ofview (outlined in dashed lines). The virtual assistant device 126 isfurther configured to transmit the input media 110 to the enhancedproduct recommendation service 102 for analysis in accordance with thetechniques disclosed herein. In some implementations, the enhancedproduct recommendation service 102 may be integrated into the virtualassistant device 126 such that the analysis of the input media 110occurs at the same device which generated the input media 110. Exemplaryvirtual assistant devices include, but are not limited to, an AMAZONECHO LOOK smart speaker connected to AMAZON ALEXA, an APPLE IPHONE smartphone connected to APPLE SIRI, or any other type of electronic devicesuitable for observing the user 124 engage in an activity.

The enhanced product recommendation service 102 may include an activityidentification engine 104 that is configured to deploy an imagerecognition model 106 to determine activity characteristics data 108associated with the activity the user 124 is engaging in. The activityidentification engine 104 may utilize various machine learningtechniques and/or “artificial intelligence” techniques to analyze theinput media 110. In some embodiments, the image recognition model 106may be created by employing supervised learning techniques wherein oneor more humans assist in generating training data associated withidentifying the activity and/or characteristics of the activity. Forexample, a human reviewer may manually analyze various instances ofinput media 110 and label certain instances as corresponding to certainactivities. As another example, a human reviewer may label certaininstances as corresponding to a particular product (e.g., a productusable to assist with whatever activity is being performed in thatinstance of input media 110). The human reviewer may examine an instanceof input media 110 and determine that an observed user is performing aslicing activity with respect to a particular type of raw material(e.g., a vegetable) and, based on this examination, the human reviewermay label this instance of input media 110 as corresponding to aparticular type of equipment (e.g., a highly rated mandolin slicer).

Then, based on the training data, the activity identification engine 104may update the image recognition model 106 and deploy the “updated”image recognition model 106 against new instances of input media 110.For example, using the “updated” image recognition model 106 to analyzethe new instances of input media 110, the activity identification engine104 may determine that a user is performing the same and/or a similaraction to that previously observed and labeled by the human reviewer.Thus, the activity identification engine 104 may automatically (e.g.,without a human reviewer's input) determine that the user in the newinstance(s) of input media 110 is performing an activity that could bedone more easily using the particular type of equipment (e.g., thehighly rated mandolin slicer).

Additionally, or alternatively, other machine learning techniques mayalso be utilized, such as unsupervised learning, semi-supervisedlearning, classification analysis, regression analysis, clustering, etc.One or more predictive models may also be utilized, such as a groupmethod of data handling, Naïve Bayes, k-nearest neighbor algorithm,majority classifier, support vector machines, random forests, boostedtrees, Classification and Regression Trees (CART), neural networks,ordinary least square, and so on.

Deploying the activity identification engine 104 to analyze the inputmedia 110 enables identification of a variety of characteristicsassociated with the activity being performed by the user 124. Forexample, the enhanced product recommendation service 102 may deploymachine learning techniques to analyze the photographs 112 and/or video114 captured by the virtual assistant device 126 and, based thereon, togenerate activity characteristics data 108 that indicates variouscharacteristics of the activity that the user 124 is observedperforming.

In some implementations, the enhanced product recommendation service 102may generate activity characteristics data 108 that indicates rawmaterial characteristics associated with raw material(s) 140 that arebeing used in performance of the observed activity. For example, theenhanced product recommendation service 102 may analyze the input media110 to identify a material that is graphically represented within theinput media 110 due to being within the field of view of the virtualassistant device 126. The enhanced product recommendation service 102may then determine whether the “identified” material is being used as amaterial input for the activity. In some implementations, machinelearning and/or artificial intelligence techniques may be deployed toimplement the image recognition model 106 for identifying differentmaterials that are represented within the photographs 112 and/or video114. For example, in the illustrated example in which the user 124 isobserved manually cutting French fries, the enhanced productrecommendation service 102 may use the image recognition model 106 toidentify that whole potatoes are shown within the input media 110.

Based on the user 124 interacting with an identified material, theenhanced product recommendation service 102 may determine whether theidentified material is a raw material that is being used as an input forthe activity. In the illustrated example, because the user 124 isconverting the whole potatoes into a different form (e.g., Frenchfries), the enhanced product recommendation service 102 may determinethat the whole potatoes are an input of the activity in the sense thatthe activity “consumes” the whole potatoes. In some implementations, araw material may be specifically identified such that the raw materialcharacteristics indicate the raw material with a high degree ofspecificity (e.g., by precise name such as Russet Burbank potato orMaris Piper potato). Additionally, or alternatively, a raw material maybe generally identified such that the raw material characteristicsindicate the raw material with a relatively lower degree of specificity(e.g., by a vegetable classification such as Root vegetables or Bulbvegetables).

As used herein, the term “raw material” refers broadly to any materialthat is worked upon during an activity to be converted from a raw formto a finished form. For example, whole potatoes may be worked upon(e.g., sliced) by the user 124 for conversion into French fries. Asanother example, fresh paint may be worked upon (e.g. applied via abrush and/or roller) by the user 124 for conversion into a paint layerthat is adhered to a wall.

In some implementations, the enhanced product recommendation service 102may generate activity characteristics data 108 that indicates outputcharacteristics associated with an output 142 that results from the user124 performing the activity. For example, the enhanced productrecommendation service 102 may analyze the input media 110 to identify aproduct that is graphically represented within the input media 110 dueto being within the field of view of the virtual assistant device 126.The enhanced product recommendation service 102 may then determinewhether the product is produced as a result of the activity. Forexample, under circumstances in which a human reviewer manuallygenerates training data, the human reviewer may analyze instances of theinput media 110 to label an identified product as an output of anactivity. It can be appreciated that under many circumstances an outputof an activity will not exist at the outset of the activity. Forexample, in the illustrated scenario the output 142 (e.g., French fries)will not exist until the user 124 has begun slicing the raw material 140(e.g., whole potatoes). Thus, based on the human generated trainingdata, the activity identification engine 104 may observe that a productis first identifiable only after the user 124 has begun performingaspects of an identified activity, and furthermore that the product isinitially identified at a location within the field of view near wherethe user 124 is seen performing the aspects of the activity. Forexample, French fries may become unidentifiable one-by-one at the samelocation within the field of view as where the user 124 is observedmanipulating a piece of equipment 138 (e.g., a Kitchen Knife). Then,based on the combination of these observations, the activityidentification engine 104 may determine (e.g., infer and/or conclude)that the product is an output of the activity.

As used herein, the term “output” refers broadly to any tangible itemthat comes into existence as a result of a person performing aparticular activity. For example, French fries (e.g., an output) maycome into existence as a result of a person slicing whole potatoes (e.g.a particular activity). As another example, a paint layer may come intoexistence as a result of a person applying paint to a surface (e.g., awall).

In some implementations, the enhanced product recommendation service 102may generate activity characteristics data 108 that indicates tasksequence characteristics associated with a task and/or series of tasksthat are performed by the user 124 to complete the activity. Forexample, the enhanced product recommendation service 102 may analyze theinput media 110 to identify task(s) that are being performed by the user124 within the field of view of the virtual assistant device 126. Insome implementations, one or more tasks performed by the user 124 may beidentified based on equipment 138 that is graphically represented withinthe input media 110. For example, the enhanced product recommendationservice 102 may determine that the user 124 is performing a series ofcutting tasks based on identifying that the user 124 is manipulating akitchen knife. In some implementations, tasks may be identified based onmovements performed by the user 124 with respect to an identified itemof equipment 138, an identified raw material 140, and/or an identifiedoutput 142. For example, continuing with the scenario in which the inputmedia 110 is of the user 124 manually cutting French fries, the enhancedproduct recommendation service 102 may determine that the user 124 isperforming the series of cutting tasks based on identifying that theuser 124 grips a whole potato (e.g., raw material) with a left handwhile manipulating a kitchen knife (e.g., equipment) with a right handto generate French fries (e.g., output). As another example, theenhanced product recommendation service 102 may determine that the user124 is performing a painting task based on identifying that the user 124is manipulating a paint brush (e.g., equipment) with respect to a paintcan (e.g., raw material) and a wall (e.g., raw material).

As used herein, the term “task sequence” refers broadly to anyprogression of one or more tasks that are identified as being performedby a user in association with a particular activity. An exemplary tasksequence may include a first task of gripping a raw material 140, asecond task of working on (e.g., cutting, shaping, stirring, kneading,heating, etc.) the raw material 140 with a piece of equipment 138,and/or a third task of moving an output 142 into a container (e.g.,placing French fries into a bowl or fryer vat). In some instances, atask sequence may include repeating one or more individual steps (e.g.,repeatedly cutting a raw material). For example, if the user 124 iscreating the French fries one-by-one, then the user 124 will of coursehave to repeat the second task (e.g., working on the raw material 140)at least once for each individual French fry slice created.

Based on the activity characteristics data 108 that is determined inassociation with the activity the user 124 is engaging in, the enhancedproduct recommendation service 102 may identify a product 134 that isusable is association with the activity. Generally, the product 134 thatis identified is designed to improve an ability to easily perform theactivity by, for example, reducing the amount of time the activitytakes, improving ergonomics of performing the activity, increasing anoutput rate of the activity, eliminating a need to perform one or moretasks of a determined activity task sequence, etc. In someimplementations, the enhanced product recommendation service 102 mayanalyze product data 116 that may be associated with one or more retailservices to identify an existing product that is usable for generatingthe output 142 that results from the activity. For example, based onidentifying that the user 124 is slicing whole potatoes intoconventionally shaped French fries, the enhanced product recommendationservice 102 may identify an existing mandolin slicer that isspecifically designed for (among other things) converting whole potatoesinto conventionally shaped French fries. In some implementations, theenhanced product recommendation service 102 may identify multipledifferent existing products that are usable in association with theactivity. For example, based on identifying that the user 124 is slicingwhole potatoes into conventionally shaped French fries, the enhancedproduct recommendation service 102 may identify both a mandolin slicerand a lever-operated restaurant quality French fry cutter.

In various implementations, the enhanced product recommendation service102 may determine parameters 118 associated with one or more productsthat are identified as being usable in association with the activity.Exemplary parameters 118 include, but are not limited to, productdescription data indicating a description of the product (e.g., aproduct-name, product-category, etc.), pricing data that indicates acost of a product, versatility data that indicates whether a product canbe adapted to different functionalities and/or activities, user reviewdata indicating whether previous users have been satisfied with aproduct, output rate data indicating an amount of output a product canproduce in a period of time (e.g., one-pound of French fries perone-minute), and/or any other parameter.

In various implementations, a product recommendation 120 may then begenerated to recommend the product 134 to the user 124 and/or to informthe user 124 of parameters 118 (e.g., price, features, usertestimonials, etc.) of the product 134. For example, in response toanalyzing the input media 110 and determining that the user 124 ismanually cutting French fries, the enhanced product recommendationservice 102 may transmit a product recommendation 120 to the virtualassistant device 126 and/or user device 128 associated with the user124. An exemplary product recommendation may inform the user 124 of theproduct 134 and/or benefits of using the product 134 as opposed toperforming the activity in the manner observed via the input media 110.As a specific but non-limiting example, a product recommendation 120could recite: “Hello, we recently noticed that you cut eight rawpotatoes into French fries and that this took you about thirty minutes.We also notice that you make French fries twice a week (or some otherperiodicity). We would like to recommend these two products which couldhelp you make French fries easier and faster. Product 1: Using thisMandolin slicer that costs $10, you could have sliced the eight rawpotatoes into French fries in ten minutes. Product 2: Using thislever-operated restaurant quality French fry cutter that costs $30, youcould have sliced the eight raw potatoes into French fries in oneminute.” Thus, it can be appreciated that in some implementations, theenhanced product recommendation service 102 may determine an amount oftime that the user 124 spent to perform the observed activity in themanner graphically represented within the input media 110. The enhancedproduct recommendation service 102 may also determine an estimatedamount of time that the user 124 could perform the activity and/orachieve the same result as the activity (e.g., generate a predeterminedamount of output) using one or more identified products.

In some implementations, the product recommendation 120 may enable theuser 124 to generate a recommendation reply 122 that indicates whetherthe user 124 would like to generate an order 130 for the product 134.For example, the product recommendation 120 may include a user interfaceelement (e.g., a virtual button) that is selectable by the user 124 tocause the order 130 to be sent to a fulfillment center 132 thatcorresponds to a retail service (e.g., AMAZON.COM INC., JET.COM, etc.).Then, responsive to the order 130, the product 134 may be shippeddirectly from the fulfillment center 132 to a physical address 136 ofthe user 124 (e.g. a home and/or office of the user 124).

In some instances, the enhanced product recommendation service 102 maycause the product 134 to be shipped to the physical address 136 withoutsending the product recommendation 120 to the user 124 and/or receivingthe recommendation reply 122 from the user 124. In a specific butnonlimiting example, the product recommendation 120 may include anindication of a preemptive delivery that has either been scheduled orwill be scheduled without contrary instructions being received from theuser 124. For example, the user 124 may send the recommendation reply122 to instruct the enhanced product recommendation service 102 not toship the product 134. Thus, if no action is taken by the user 124 inresponse to receiving the product recommendation 120, the product 134may be automatically delivered to the user 124 even without the user 124generating an order for the customized product 134.

Turning now to FIG. 2, illustrated is exemplary input media 200 that issuitable for analysis by the activity identification engine 104 todetermine exemplary activity characteristics data 108. The exemplaryinput media 200 may graphically represent a user 124 engaging in anactivity. As illustrated, the exemplary input media 200 graphicallyrepresents the user 124 operating an item of equipment 138 forconverting a raw material 140 into an output 142. In this example, theactivity identification engine 104 analyzes the exemplary input media200 which may include the video 114 that is generated by the virtualassistant device 126. By deploying machine learning techniques toanalyze the video 114 using the image recognition model 106, theactivity identification engine 104 identifies various items to generateidentified items data 202. In the specifically illustrated butnonlimiting example, the activity identification engine 104 may identifyeach of whole potatoes, the hands of the user 124, a kitchen knife, anda pile of uncooked French fries. In some instances, the activityidentification engine 104 may classify and/or label identified items.For example, as illustrated, the identified items data 202 includeslabels that are individually associated with identified items. That is,the first identified item of “hands” is labeled as the user 124, thesecond identified item of “Kitchen Knife” is labeled as the equipment138, etc.

In various implementations, the activity identification engine 104 maygenerate identified activity data 204 that indicates one or moreactivities that correspond to and/or are associated with the variousidentified items. For example, upon identification of the French fries,the activity identification engine 104 may analyze one or more databasesto determine various information about French fries such as, forexample, what they are made of, how they can be made, what types ofequipment are used to make them, etc. In this specific example, theactivity identification engine 104 may determine that French fries aremade from potatoes. Then, based on having also identified the wholepotatoes, the activity identification engine 104 may determine with somelevel of confidence that the user 124 is engaging in an activity toconvert the whole potatoes into French fries.

Thus, in some instances, the activity identification engine 104 mayinfer an activity being performed by the user 124 simply fromidentifying one or more items within the exemplary input media 200. Inother instances, the activity identification engine 104 may determinethe activity being performed by the user 124 based on observations ofhow the user 124 is interacting with and/or manipulating one or moreidentified items. For example, in the illustrated example, the activityidentification engine 104 may observe that the user 124 is manipulatinga kitchen knife with respect to a whole potato in a manner that resultsin additional individual French fries becoming identifiable as the video114 progresses. Based on these observations, the activity identificationengine 104 may determine with a particular level of confidence that theuser 124 is using the identified kitchen knife to manually slice wholepotatoes into French fries on a one-by-one basis. For example, asillustrated, the activity identification engine 104 has determined withninety-nine percent confidence that the user 124 is “manually cuttingFrench fries.”

Then, based on having identified the activity being performed by theuser 124, the enhanced product recommendation service 102 and/oractivity identification engine 104 thereof may determine related productdata 206 that indicates one or more products that are usable inassociation with the activity and/or to produce a similar result (e.g.,output) as the activity. In some instances, the related product data 206that is generated based on analyzing the exemplary input media 200 mayindicate multiple products that are usable in association with theactivity and/or to generate the output 142 identified in the exemplaryinput media 200. In the illustrated example, the related product data206 indicates that both a mandolin slicer and a lever operated Frenchfry cutter are usable in association with the activity and/or togenerate the output 142 identified by analyzing the exemplary inputmedia 200.

Turning now to FIG. 3, illustrated is an exemplary system 300 foranalyzing input media 110 of a user 124 engaging in an activity anddetermining physical measurements associated with various aspects of theactivity. For example, the system 300 may identify measurements (e.g.,in terms of length, width, height, etc.) of a piece of equipment 138that the user 124 is operating to engage in the activity. As anotherexample, the system 300 may identify measurements between one or moreitems that are identified within the input media 110. Based on theidentified measurements, the system 300 may determine productcustomization parameters 306 for generating a customized product 310that is designed to assist the user 124 perform the activity.

For purposes of the discussion of FIG. 3, the user 124 is shown to beoperating first equipment 138(1) to position a user device 128 so thatthe user 124 is within a field of view of a camera of the user device128. As further illustrated in FIG. 3, the user 124 is also shown tothrow and catch second equipment 138(2) within the field of view of theuser device 128. More particularly, in FIG. 3 the user 124 is shown asholding a selfie stick (e.g. the first equipment 138(1)) between a leftarm and a torso in order to position the user device 128 for taking a“selfie” while concurrently “juggling” three balls (e.g., the secondequipment 138(2)). Exemplary user devices include, but are not limitedto, smart phones, tablet computers, laptop computers, virtual assistantdevices, or any other type of electronic device that is suitable forobserving the user 124 engage in an activity and/or receiving a productrecommendation 120.

The system 300 may include a version of the enhanced productrecommendation service 102 which comprises a product customizationengine 302 for generating the product customization parameters 306. Asillustrated, the enhanced product recommendation service 102 may receivethe input media 110 from the user device 128 and may then analyze theinput media 110 in accordance with various techniques described inrelation to FIGS. 1 and 2. For example, in the illustrated scenario, theenhanced product recommendation service 102 may identify the firstequipment 138(1) and, based thereon, determine some aspects of theactivity being performed by the user 124 (e.g., that the user 124 istaking a “selfie”). The enhanced product recommendation service 102 mayalso identify the second equipment 138(2) and, based thereon, determineother aspects of the activity being performed by the user 124 (e.g.,that the user 124 is attempting to take a “selfie” of himself juggling).

The enhanced product recommendation service 102 may deploy the productcustomization engine 302 to analyze the activity being performed by theuser 124 within the input media 110 and identify various aspects of theactivity that can be improved upon by specifically designing a productto assist with the activity. For example, in the illustrated scenario,the product customization engine 302 may determine that the range ofmotion of the left hand of the user 124 is restricted as compared to theright hand of the user 124. The product customization engine 302 mayfurther determine that the cause of this restricted range of motion isthe way that the user 124 is holding the first equipment 138(1) (e.g.,awkwardly squeezing the selfie stick between the left arm and thetorso). As described in more detail with regard to FIG. 4B, the productcustomization engine 302 may then determine product customizationparameters 306 for generating a custom product 310 that mitigates one ormore aspects of the activity being performed by the user 124. Forexample, based on observing that the manner in which the user 124 isholding the first equipment 138(1) results in some inadvertentdifficultly (e.g., the restricted range of motion), productcustomization parameters 306 may be determined for generating acustomized product 310 that eliminates the need for the user 124 to holdthe first equipment 138(1) in order to position the user 124 within thefield of view of the user device 128.

The product customization engine 302 may further analyze the input media110 to generate physical measurements data 304 in association withvarious aspects of the activity being performed by the user 124. Forexample, in the illustrated scenario, the product customization engine302 may determine a length measurement association with the firstequipment 138(1). Additionally, or alternatively, the productcustomization engine 302 may determine a distance measurement betweentwo or more identified items. For example, a distance between variouspieces of the second equipment 138(2) can be determined. In variousimplementations, the product customization parameters 306 may bedetermined based at least in part on the physical measurements data 304.For example, in the illustrated scenario, the product customizationengine 302 may observe that the individual pieces of second equipment138(2) exist and then reenter the field of view of the user device 128toward the top of their trajectory. Therefore, the product customizationengine 302 may generate product customization parameters 306 for acustomized “selfie-stick” that is relatively longer than the“selfie-stick” the user 124 is observed to operate in the input media110 so that the second equipment 138(2) remains visible within the fieldof view of the user device 128 throughout the user 124 performing theactivity.

The product customization parameters 306 may then be transmitted to acustomization facility 308 for generating the customized product 310that is specifically designed by the enhanced product recommendationservice 102 based on observing the user 124 perform an activity. In someimplementations, a product recommendation 120 may be generated torecommend the customized product 310 to the user 124 and/or to informthe user 124 of various aspects of the customized product 310. Forexample, the product recommendation 120 may include a graphicalrepresentation of the customized product 310 (which may be a computerrendered representation using machine learning techniques describedherein prior to the customized product 310 being physicallymanufactured). The product recommendation 120 may further includedescriptive details of how the customized product 310 is designed toimprove the ability of the user 124 to perform the observed activity.

As illustrated, the product recommendation 120 may be transmitted fromthe enhanced product recommendation service 102 to the user device 128.Then, responsive to user input received in association with the productrecommendation 120, a recommendation reply 122 may be transmitted fromthe user device 128 back to the enhanced product recommendation service102 with an indication as to whether the user 124 would like to generatean order for the customized product 310. Ultimately, the customizationfacility 308 may be caused or otherwise instructed to manufacture thecustomized product 310 and furthermore to deliver the customized product310 to a physical address 136 associated with the user 124. In someimplementations, the enhanced product recommendation service 102 maycause the customized product 310 to be shipped to the physical address136 preemptively (e.g., without sending a product recommendation 120 tothe user 124 and receiving the recommendation reply 122 from the user124).

Turning now to FIG. 4A, illustrated is exemplary input media 400 that issuitable for analysis by the enhanced product recommendation service 102and/or one or more components thereof to determine exemplary productcustomization parameters 306. As illustrated, the exemplary input media400 graphically represents the user 124 holding a “selfie-stick” betweena left arm and a torso while concurrently juggling three balls. In thisexample, the activity identification engine 104 analyzes the exemplaryinput media 400 which may include video 114 that is generated by userdevice 128. By deploying machine learning techniques to analyze thevideo 114 using the image recognition model 106, the activityidentification engine 104 is able to identify various items to generateidentified items data 402. The activity identification engine 104 mayclassify and/or label various identified items. As illustrated, theidentified items data 402 includes labels that are individuallyassociated with hands of the user 124, arms of the user 124, aselfie-stick, and the three identified balls. As further describedabove, the activity identification engine 104 may generate identifiedactivity data 404 that indicates one or more activities that the user124 is performing. For example, as illustrated, the identified activitydata 404 indicates that the user is performing a first task of holdingthe selfie-stick between an arm and a torso while concurrentlyperforming a task activity of juggling the three identified balls.

In various implementations, the product customization engine 302 mayreceive and analyze the exemplary activity characteristics data 108and/or the exemplary input media 400 in order to generate productcustomization parameters 306. The product customization parameters 306may indicate one or more components which may be combined to generatethe customized product 310. For example, as illustrated, the productcustomization parameters 306 indicate that the customized product 310may be generated by combining a first component that is a selfie-stickof one and one-half meters in length as well as a second component thatis a waist belt with an adjustable pole clamp.

Turning now to FIG. 4B, illustrated is an exemplary customized product310 that specifically corresponds to the scenario discussed in relationto FIG. 4A. In various implementations, the product customizationparameters 306 may be configured to enable the user 124 to performsubstantially the same activity observed from analyzing the exemplaryinput media 400 while eliminating a need of the user 124 to perform oneor more sub-activities of the observed activity. For example, in thespecific but nonlimiting scenario illustrated in FIGS. 4A and 4B, theexemplary customized product 310 enables the user 124 to perform theactivity of juggling the three balls while holding a user device 128 inan optimal position for taking a “selfie” photograph and/or video butwithout having to perform the first identified activity of holding the“selfie” stick between the left arm and the torso. In particular, asillustrated, the exemplary customized product 310 is shown to be acombination of a first product component that the user 124 may weararound his waist. The illustrated exemplary customized product 310 isshown to further include a second product component that can be affixedto the first product component and to the user device 128 (not shown) toenable the user 124 to maintain the user device 128 in a position thatis suitable for taking the “selfie” photographs and/or video withouthaving to awkwardly hold the “selfie” stick as observed in the exemplaryinput media 400.

In some implementations, the enhanced product recommendation service 102may generate a computer-generated-image of the customized product basedon the product customization parameters. For example, the image of theuser 124 engaging in the activity that is shown in FIG. 4B may be acomputer-generated-image that includes a computer rendering of thecustomized product 310. Then, the enhanced product recommendationservice 102 may transmit the computer-generated-image of the customizedproduct to the user 124 within the product recommendation 120.

FIGS. 5 and 6 illustrate flow diagrams in association with exampleprocesses 500 and 600 which are described with reference to FIGS. 1-4B.The processes are illustrated as a collection of blocks in a logicalflow graph, which represent a sequence of operations that can beimplemented in hardware, software, or a combination thereof. In thecontext of software, the blocks represent computer-executableinstructions that, when executed by one or more processors, perform therecited operations. Generally, computer-executable instructions includeroutines, programs, objects, components, data structures, and the likethat perform or implement particular functions. The order in whichoperations are described is not intended to be construed as alimitation, and any number of the described blocks can be combined inany order and/or in parallel to implement the process. Other processesdescribed throughout this disclosure shall be interpreted accordingly.

FIG. 5 illustrates an example process 500 of analyzing input media toidentify an activity being performed by a user and, based thereon,identifying one or more existing products that facilitate performance ofthe identified activity.

At block 501, a system may receive input media 110 of a user 124engaging in an activity. Exemplary input media may include a graphicalrepresentation of the user performing the activity such as, for example,photographs 112 and/or video 114 of the user 124 performing theactivity. As described above, in various instances the input media 110may be obtained from a virtual assistant device 126 that is set upwithin an environment (e.g., a kitchen, a woodshop, a garage, etc.) toobserve the user 124. In some instances, the virtual assistant device126 may be a smart phone device that the user 124 operates to takephotographs and/or video of oneself.

At block 503, the system may analyze the input media to generateactivity characteristics data associated with the activity that isperformed by the user. In some implementations, the activitycharacteristics data indicates one or more of: task sequencecharacteristics that indicate a plurality of tasks that are completed bythe user during performance of the activity, raw materialcharacteristics associated with raw material(s) that are being used inperformance of the observed activity, equipment characteristicsassociated with one or more identified items of equipment that the useris operating to perform the activity, physical measurements associatedwith various aspects of the activity, and/or output characteristics thatindicate an output that is produced by the user during performance ofthe activity. In some implementations, generating the activitycharacteristics data includes identifying an activity that is beingperformed by the user. For example, an activity identification engine104 may be deployed to identify that the user is converting wholepotatoes into French fries. As another example, the activityidentification engine 104 may be deployed to identify that the user isrecording himself juggling (e.g., taking a juggling “selfie”).

At block 505, the system may identify an existing product that is usableto facilitate performance of the activity. For example, the system mayparse product data 116 to identify an existing product that is described(e.g., within a product description) as being efficient at facilitatingthe activity. As a specific but non-limiting example, undercircumstances in which the activity that the user is performing isidentified as being “converting whole potatoes into French fries,” thesystem may parse the product data to identify a lever-operatedrestaurant quality French fry cutter that is described as being able to“Slice Potatoes into perfectly sized French fries faster than any otherproduct on the market today!”

At block 507, the system may communicate aspects of the identifiedexisting product to the user 124. For example, the system may send aproduct recommendation 120 to the user 124 via the virtual assistantdevice 126 and/or the user device 128. In some implementations, theproduct recommendation 120 may be an audible recommendation that isplayed aloud to the user 124 via the virtual assistant device 126. Forexample, the virtual assistant device could recite: “Hello, we recentlynoticed that you cut eight raw potatoes into French fries and that thistook you about thirty minutes. If you were to use a lever-operatedrestaurant quality French fry cutter that costs $30, you could havesliced the eight raw potatoes into French fries in one minute.”

FIG. 6 illustrates an example process 600 of analyzing input media togenerate activity characteristics data associated with an activity andphysical measurements data associated with the activity and, basedthereon, to generate product customization parameters for generating acustomized product that facilitates performance of the activity.

At block 601, a system may analyze input media to generate activitycharacteristics data associated with the activity that is performed bythe user. Generating the activity characteristics data includesidentifying an activity that is being performed by the user.

At block 603, the system may analyze the input media to generatephysical measurements data associated with the activity. In someimplementations, the physical measurements data may indicate dimensionsassociated with an output of the activity. For example, undercircumstances in which the user is slicing French fries, the system mayanalyze the input media to determine a nominal size of the individualFrench fries that the user produces. In some implementations, thephysical measurements data may indicate dimensions between two or moreobjects that are identified within the input media. For example, undercircumstances in which the user is reaching up to grab an item from anupper cabinet, the system may identify a distance between the user andthe item.

At block 605, the system may generate, based on the activitycharacteristics data and the physical measurements data, productcustomization parameters for generating a customized product thatfacilitates the activity. For example, the product customizationparameters may define product specifications for a Mandolin slicer thatis specifically configured to generate French fries at a nominal sizeidentified by analyzing the input media. As another example, the productcustomization parameters may define a length for a tool that isconfigured to enable the user to grab the item from the upper cabinetand that is made at a length that is selected based on measurements ofthe upper cabinet taken by analyzing the input media.

Then, at block 607, the system may communicate aspects of the customizedproduct, that is customized based on the customization parameters, tothe user and/or a customization facility. In some instances,communicating aspects of the customized product to the user may includecausing the customized product to be preemptively manufactured anddelivered to the user. In some instances, communicating aspects of thecustomized product to the user may include generating a productrecommendation that includes a description and/or graphicalrepresentation of the customized product and, ultimately, transmittingthe notification to the user.

FIG. 7 shows additional details of an example computer architecture fora computer capable of executing the functionalities described hereinsuch as, for example, those described with reference to the enhancedproduct recommendation service 102, or any program components thereof asdescribed herein. Thus, the computer architecture 700 illustrated inFIG. 7 illustrates an architecture for a server computer, or network ofserver computers, or any other types of computing devices suitable forimplementing the functionality described herein. The computerarchitecture 700 may be utilized to execute any aspects of the softwarecomponents presented herein.

The computer architecture 700 illustrated in FIG. 7 includes a centralprocessing unit 702 (“CPU”), a system memory 704, including arandom-access memory 706 (“RAM”) and a read-only memory (“ROM”) 708, anda system bus 710 that couples the memory 704 to the CPU 702. A basicinput/output system containing the basic routines that help to transferinformation between elements within the computer architecture 700, suchas during startup, is stored in the ROM 708. The computer architecture700 further includes a mass storage device 712 for storing an operatingsystem 714, other data, and one or more application programs. The massstorage device 712 may further include one or more of the activityidentification engine 104, image recognition model 106, and/or productcustomization engine 302.

The mass storage device 712 is connected to the CPU 702 through a massstorage controller (not shown) connected to the bus 710. The massstorage device 712 and its associated computer-readable media providenon-volatile storage for the computer architecture 700. Although thedescription of computer-readable media contained herein refers to a massstorage device, such as a solid-state drive, a hard disk or CD-ROMdrive, it should be appreciated by those skilled in the art thatcomputer-readable media can be any available computer storage media orcommunication media that can be accessed by the computer architecture700.

Communication media includes computer readable instructions, datastructures, program modules, or other data in a modulated data signalsuch as a carrier wave or other transport mechanism and includes anydelivery media. The term “modulated data signal” means a signal that hasone or more of its characteristics changed or set in a manner as toencode information in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of the any of the aboveshould also be included within the scope of computer-readable media.

By way of example, and not limitation, computer storage media mayinclude volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage of information suchas computer-readable instructions, data structures, program modules orother data. For example, computer media includes, but is not limited to,RAM, ROM, EPROM, EEPROM, flash memory or other solid state memorytechnology, CD-ROM, digital versatile disks (“DVD”), HD-DVD, BLU-RAY, orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store the desired information and which can be accessed bythe computer architecture 700. For purposes of the claims, the phrase“computer storage medium,” “computer-readable storage medium” andvariations thereof, does not include waves, signals, and/or othertransitory and/or intangible communication media, per se.

According to various techniques, the computer architecture 700 mayoperate in a networked environment using logical connections to remotecomputers through a network 750 and/or another network (not shown). Thecomputer architecture 700 may connect to the network 750 through anetwork interface unit 716 connected to the bus 710. It should beappreciated that the network interface unit 716 also may be utilized toconnect to other types of networks and remote computer systems. Thecomputer architecture 700 also may include an input/output controller718 for receiving and processing input from a number of other devices,including a keyboard, mouse, or electronic stylus (not shown in FIG. 7).Similarly, the input/output controller 718 may provide output to adisplay screen, a printer, or other type of output device (also notshown in FIG. 7). It should also be appreciated that via a connection tothe network 750 through a network interface unit 716, the computingarchitecture may enable the enhanced product recommendation service 102,the virtual assistant device 126, the user device 128, the customizationfacility 308, and/or the fulfillment center 132 to communicate with oneanother.

It should be appreciated that the software components described hereinmay, when loaded into the CPU 702 and executed, transform the CPU 702and the overall computer architecture 700 from a general-purposecomputing system into a special-purpose computing system customized tofacilitate the functionality presented herein. The CPU 702 may beconstructed from any number of transistors or other discrete circuitelements, which may individually or collectively assume any number ofstates. More specifically, the CPU 702 may operate as a finite-statemachine, in response to executable instructions contained within thesoftware modules disclosed herein. These computer-executableinstructions may transform the CPU 702 by specifying how the CPU 702transitions between states, thereby transforming the transistors orother discrete hardware elements constituting the CPU 702.

Encoding the software modules presented herein also may transform thephysical structure of the computer-readable media presented herein. Thespecific transformation of physical structure may depend on variousfactors, in different implementations of this description. Examples ofsuch factors may include, but are not limited to, the technology used toimplement the computer-readable media, whether the computer-readablemedia is characterized as primary or secondary storage, and the like.For example, if the computer-readable media is implemented assemiconductor-based memory, the software disclosed herein may be encodedon the computer-readable media by transforming the physical state of thesemiconductor memory. For example, the software may transform the stateof transistors, capacitors, or other discrete circuit elementsconstituting the semiconductor memory. The software also may transformthe physical state of such components in order to store data thereupon.

As another example, the computer-readable media disclosed herein may beimplemented using magnetic or optical technology. In suchimplementations, the software presented herein may transform thephysical state of magnetic or optical media, when the software isencoded therein. These transformations may include altering the magneticcharacteristics of particular locations within given magnetic media.These transformations also may include altering the physical features orcharacteristics of particular locations within given optical media, tochange the optical characteristics of those locations. Othertransformations of physical media are possible without departing fromthe scope and spirit of the present description, with the foregoingexamples provided only to facilitate this discussion.

In light of the above, it should be appreciated that many types ofphysical transformations take place in the computer architecture 700 inorder to store and execute the software components presented herein. Italso should be appreciated that the computer architecture 700 mayinclude other types of computing devices, including hand-held computers,embedded computer systems, personal digital assistants, and other typesof computing devices known to those skilled in the art. It is alsocontemplated that the computer architecture 700 may not include all ofthe components shown in FIG. 7, may include other components that arenot explicitly shown in FIG. 7, or may utilize an architecturecompletely different than that shown in FIG. 7.

CONCLUSION

In closing, although the various techniques have been described inlanguage specific to structural features and/or methodological acts, itis to be understood that the subject matter defined in the appendedrepresentations is not necessarily limited to the specific features oracts described. Rather, the specific features and acts are disclosed asexample forms of implementing the claimed subject matter.

What is claimed is:
 1. A computer-implemented method, comprising:receiving, from a camera associated with at least one computing device,input media that includes a graphical representation of a userperforming an activity; generating, by the at least one computingdevice, activity characteristics data by processing the input mediausing a trained image recognition model configured to automaticallyidentify the activity, the activity characteristics data defining: tasksequence characteristics that indicate a plurality of tasks that areperformed by the user during performance of the activity; and outputcharacteristics that indicate an output that is produced by the userresulting from performance of the activity; determining, by the at leastone computing device, a first duration of time indicating an amount oftime spent by the user during the performing the activity based on theactivity characteristics data; determining, by the at least onecomputing device, parameters for a product based on the activitycharacteristics data, the product being usable for completing theactivity without performing at least one of the plurality of tasks andin a second duration of time that is less than the first duration oftime; and responsive to determining the parameters for the product basedon the activity characteristics data communicating, by the at least onecomputing device, a message to a device disposed with the user, themessage specifying the product and describing a difference between thefirst and second durations of time.
 2. The computer-implemented methodof claim 1, wherein the activity characteristics data further definesraw material characteristics that indicate one or more raw materialsthat are converted via performance of the activity.
 3. Thecomputer-implemented method of claim 2, wherein determining theparameters for the product is further performed based on the rawmaterial characteristics.
 4. The computer-implemented method of claim 1,wherein the trained image recognition model is configured to identifythe activity by: identifying at least one item of equipment that is usedby the user during performance of the activity; and determining one ormore movements that are performed by the user with respect to the atleast one item of equipment, wherein the task sequence characteristicsare generated based at least in part on the one or more movements. 5.The computer-implemented method of claim 1, wherein the input mediaincludes at least one of a photograph that is captured by the camera ora video that is captured by the camera.
 6. The computer-implementedmethod of claim 1, wherein the message further comprises an indicationof at least one benefit of using the product versus performing theactivity as represented in the input media.
 7. The computer-implementedmethod of claim 1, wherein the message further comprises an indicationof the first duration of time and the second duration of time.
 8. Asystem comprising: one or more processors; a camera; and a memorystoring computer-readable instructions that are executable by the one ormore processors to perform operations comprising: receiving input mediafrom the camera, the input media including a graphical representation ofa user performing an activity; generating activity characteristics databy processing the input media using a trained image recognition modelconfigured to automatically identify the activity, the activitycharacteristics data defining: task sequence characteristics thatindicate a plurality of tasks that are performed by the user duringperformance of the activity; and output characteristics that indicate anoutput that is produced by the user resulting from performance of theactivity; determining a first duration of time indicating an amount oftime spent by the user during the performing the activity based on theactivity characteristics data; determining parameters for a productbased on the activity characteristics data, the product being usable forcompleting the activity without performing at least one of the pluralityof tasks and in a second duration of time that is less than the firstduration of time; and responsive to determining the parameters for theproduct based on the activity characteristics data communicating amessage to a client device, the message specifying the product anddescribing a difference between the first and second durations of time.9. The system of claim 8, the activity characteristics data furtherdefining raw material characteristics that indicate one or more rawmaterials that are converted via performance of the activity.
 10. Thesystem of claim 9, wherein determining the parameters for the product isfurther performed based on the raw material characteristics.
 11. Thesystem of claim 8, wherein the trained image recognition model isconfigured to identify the activity by: identifying at least one item ofequipment that is used by the user during performance of the activity;and determining one or more movements that are performed by the userwith respect to the at least one item of equipment, wherein the tasksequence characteristics are generated based at least in part on the oneor more movements.
 12. The system of claim 8, wherein the input mediaincludes at least one of a photograph captured by the camera or a videocaptured by the camera.
 13. The system of claim 8, the message furthercomprising an indication of at least one benefit of using the productversus performing the activity as represented in the input media. 14.The system of claim 8, the message further comprising an indication ofthe first duration of time and the second duration of time.
 15. Acomputer-readable storage medium storing instructions that areexecutable by one or more computing devices to perform operationscomprising: receiving, from a camera, input media that includes agraphical representation of a user performing an activity; generatingactivity characteristics data by processing the input media using atrained image recognition model configured to automatically identify theactivity, the activity characteristics data defining: task sequencecharacteristics that indicate a plurality of tasks that are performed bythe user during performance of the activity; and output characteristicsthat indicate an output that is produced by the user resulting fromperformance of the activity; determining a first duration of timeindicating an amount of time spent by the user during the performing theactivity based on the activity characteristics data; determiningparameters for a product based on the activity characteristics data, theproduct being usable for completing the activity without performing atleast one of the plurality of tasks and in a second duration of timethat is less than the first duration of time; and responsive todetermining the parameters for the product based on the activitycharacteristics data communicating a message to a client deviceassociated with the user, the message specifying the product anddescribing a difference between the first and second durations of time.16. The computer-readable storage medium of claim 15, wherein the camerais implemented by one of the one or more computing devices and the inputmedia comprises a video captured by the camera.
 17. Thecomputer-readable storage medium of claim 15, the activitycharacteristics data further defining raw material characteristics thatindicate one or more raw materials that are converted via performance ofthe activity, wherein determining the parameters for the product isfurther performed based on the raw material characteristics.
 18. Thecomputer-readable storage medium of claim 15, wherein the trained imagerecognition model is configured to identify the activity by: identifyingat least one item of equipment that is used by the user duringperformance of the activity; and determining one or more movements thatare performed by the user with respect to the at least one item ofequipment, wherein the task sequence characteristics are generated basedat least in part on the one or more movements.
 19. The computer-readablestorage medium of claim 15, the message further comprising an indicationof at least one benefit of using the product versus performing theactivity as represented in the input media.
 20. The computer-readablestorage medium of claim 15, the message further comprising an indicationof the first duration of time and the second duration of time.